Crystal structure modeling with graph neural networks is essential for various applications in materials informatics, and capturing SE(3)-invariant geometric features is a fundamental requirement for these networks. A straightforward approach is to model with orientation-standardized structures through structure-aligned coordinate systems, or “frames.” However, unlike molecules, determining frames for crystal structures is challenging due to their infinite and highly symmetric nature. In particular, existing methods rely on a statically fixed frame for each structure, determined solely by its structural information, regardless of the task under consideration. Here, we rethink the role of frames, *questioning whether such simplistic alignment with the structure is sufficient*, and propose the concept of *dynamic frames*. While accommodating the infinite and symmetric nature of crystals, these frames provide each atom with a dynamic view of its local environment, focusing on actively interacting atoms. We demonstrate this concept by utilizing the attention mechanism in a recent transformer-based crystal encoder, resulting in a new architecture called **CrystalFramer**. Extensive experiments show that CrystalFramer outperforms conventional frames and existing crystal encoders in various crystal property prediction tasks.

Predicting physical properties of materials from their crystal structures is a fundamental problem in materials science. In peripheral areas such as the prediction of molecular properties, fully connected attention networks have been shown to be successful. However, unlike these finite atom arrangements, crystal structures are infinitely repeating, periodic arrangements of atoms, whose fully connected attention results in *infinitely connected attention*. In this work, we show that this infinitely connected attention can lead to a computationally tractable formulation, interpreted as *neural potential summation*, that performs infinite interatomic potential summations in a deeply learned feature space. We then propose a simple yet effective Transformer-based encoder architecture for crystal structures called *Crystalformer*. Compared to an existing Transformer-based model, the proposed model requires only 29.4% of the number of parameters, with minimal modifications to the original Transformer architecture. Despite the architectural simplicity, the proposed method outperforms state-of-the-art methods for various property regression tasks on the Materials Project and JARVIS-DFT datasets.

This study focuses on the powder grinding process, which is a necessary step for material synthesis in materials science experiments. In material science, powder grinding is a time-consuming process that is typically executed by hand, as commercial grinding machines are unsuitable for samples of small size. Robotic powder grinding would solve this problem, but it is a challenging task for robots, as it requires observing the powder state and generating appropriate motions. Our previous study proposed a robotic powder grinding system using visual feedback. Although visual feedback is helpful for observing the powder distribution, the particle size during the grinding process remains invisible, leading to suboptimal robot actions. In some cases, the robot chose to gather the powder even though continuing to grind instead would have produced finer powder. In this paper, we present a multi-modal robotic grinding system that utilizes both audio and visual feedback. It makes use of the grinding sound which carries information about the grinding progress, as the particle size strongly affects the audio intensity. The audio feedback enables the robot to grind until the powder is sufficiently fine. In our experiments, the robot ground 80.5% of the powder to a particle size smaller than$250\ \mu\mathrm{m}$with audio and visual feedback and 68% without audio feedback, indicating that multi-modal feedback is an effective tool to produce finer powder. We conclude that the addition of audio feedback provides crucial information to the robot, allowing it to better understand the progress of the grinding process and make more optimal decisions. This robot system can be used to prepare samples in material science experiments and analyze the grinding process.

Grinding materials into a fine powder is a time-consuming task in material science that is generally performed by hand, as current automated grinding machines might not be suitable for preparing small-sized samples. This study presents a robotic powder grinding system for laboratory automation in material science applications that observe the powder's state to improve the grinding outcome. We developed a soft jig consisting of off-the-shelf gel materials and 3D-printed parts, which can be used with any robot arm to perform powder grinding. The jig's physical softness allows for safe grinding without force sensing. In addition, we developed a visual feedback system that observes the powder distribution and decides where to grind and when to gather. The results showed that our system could grind 79 percent of the powder to a particle size smaller than 200 μm by using the soft jig and visual feedback. This ratio was 57% when using only the soft jig without feedback. Our system can be used immediately in laboratories to alleviate the workload of researchers.

This paper describes a new machine-learning application to speed up Small-angle neutron scattering (SANS) experiments, and its method based on probabilistic modeling. SANS is one of the scattering experiments to observe microstructures of materials; in it, two-dimensional patterns on a plane (SANS pattern) are obtained as measurements. It takes a long time to obtain accurate experimental results because the SANS pattern is a histogram of detected neutrons. For shortening the measurement time, we propose an earlystopping method based on Gaussian mixture modeling with a prior generated from B-spline regression results. An experiment using actual SANS data was carried out to examine the accuracy of the method. It was confirmed that the accuracy with the proposed method converged 4 minutes after starting the experiment (normal SANS takes about 20 minutes).